Deep Learning Software Security and Fairness of Deep Learning SP18 Today

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Deep Learning Software Security and Fairness of Deep Learning SP18 Today ● HW1 is out, due Feb 15th ● Anaconda and Jupyter Notebook ● Deep Learning Software ○ Keras ○ Theano ○ Numpy Anaconda ● A package management system for Python Anaconda ● A package management system for Python Jupyter notebook ● A web application that where you can code, interact, record and plot. ● Allow for remote interaction when you are working on the cloud ● You will be using it for HW1 Deep Learning Software Deep Learning Software Caffe(UCB) Caffe2(Facebook) Paddle (Baidu) Torch(NYU/Facebook) PyTorch(Facebook) CNTK(Microsoft) Theano(U Montreal) TensorFlow(Google) MXNet(Amazon) Keras (High Level Wrapper) Deep Learning Software: Most Popular Caffe(UCB) Caffe2(Facebook) Paddle (Baidu) Torch(NYU/Facebook) PyTorch(Facebook) CNTK(Microsoft) Theano(U Montreal) TensorFlow(Google) MXNet(Amazon) Keras (High Level Wrapper) Deep Learning Software: Today Caffe(UCB) Caffe2(Facebook) Paddle (Baidu) Torch(NYU/Facebook) PyTorch(Facebook) CNTK(Microsoft) Theano(U Montreal) TensorFlow(Google) MXNet(Amazon) Keras (High Level Wrapper) Mobile Platform ● Tensorflow Lite: ○ Released last November Why do we use deep learning frameworks? ● Easily build big computational graphs ○ Not the case in HW1 ● Easily compute gradients in computational graphs ● GPU support (cuDNN, cuBLA...etc) ○ Not required in HW1 Keras ● A high-level deep learning framework ● Built on other deep-learning frameworks ○ Theano ○ Tensorflow ○ CNTK ● Easy and Fun! Keras: A High-level Wrapper ● Pass on a layer of instances in the constructor ● Or: simply add layers. Make sure the dimensions match. Keras: Compile and train! Epoch: 1 epoch means going through all the training dataset once Numpy ● The fundamental package in Python for: ○ Scientific Computing ○ Data Science ● Think in terms of vectors/Matrices ○ Refrain from using for loops! ○ Similar to Matlab Numpy ● Basic vector operations ○ Sum, mean, argmax…. ● Linear Algebra operations Numpy ● Indexing, Slicing, Iterating Numpy ● Broadcasting Numpy Example ● Find the nearest value from a given value in an array Computational Graphs ● f(x,y,z) = sum(x*y + z) ● x,y,z can be scalars, vectors, matrices, tensors. Computational Graphs- Numpy Computational Graphs - Theano ● Define a variable(input) ● Define new variables using Theano operations ● Define Output ● Define functions over these variables Computational Graph-Theano Define Variables Computational Graph-Theano Define New Variables Computational Graph-Theano Define functions Theano: Shared Variable and Update ● Hybrid symbolic and non-symbolic variables ● Shared between multiple functions Theano: Shared Variable and Update ● Hybrid symbolic and non-symbolic variables ● Shared between multiple functions Score Function Gradient Descent Tensorflow Define Variables Define New Variables(Gradients) Define Functions Pytorch Define Variables Define Functions Comparison Framework Pros/Cons Theano Development completed, No development in progress, Static Tensorflow Actively developed, big community, Static PyTorch Better for Research, relatively new, Dynamic Keras High-level, Easy, Not flexible Resources ● Great Documentation on all of the DL software ● Deeplearning.ai ● State-of-art result for machine learning problems ○ https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems.

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Intro to Tensorflow 2.0 MBL, August 2019
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